Industrial Vision Harnesses AI
How industrial visual inspection applications can leverage the potential of artificial intelligence (AI) tools. Written by Youssef BELGNAOUI, Editor-in-Chief of Automation France.
Industrial vision systems have long been deployed in production to perform inspections aimed at detecting anomalies, contaminants, and other irregularities in manufactured products. Traditionally, image processing relied on predefined, rule-based programming, which artificial intelligence (AI) now makes it possible to overcome. AI, and in particular deep learning, enables industrial vision to take a new step forward. It complements and sometimes surpasses rule-based vision systems in complex, unpredictable, or information-rich environments that are difficult to model manually. Thanks to its learning capacity, AI paves the way for a new generation of industrial vision applications.
Traditional industrial vision
Industrial vision systems are based on digital sensors integrated into industrial cameras equipped with specific optics to capture images. These images are then transmitted to a PC or to an embedded controller in the camera, so that dedicated software can process, analyse, and measure different characteristics for decision-making. Such vision systems perform very well with uniform parts of consistent quality. They use rule-based algorithms, processed step by step, which are more economical than large-scale human inspection. On a production line, a vision system configured in this way can inspect hundreds or even thousands of parts per minute. The results of these visual data are based on a fixed rule programming approach to solve inspection tasks. Rule-based vision works effectively with a known set of variables: the presence of a part, the distance between objects, the position of a component to be picked up by a robot. These operations are relatively easy to deploy on an assembly line in a controlled environment.
This method is well suited to controlled contexts, where parts are homogeneous, well positioned, and tolerances are consistent. It can inspect hundreds or even thousands of parts per minute with the required level of reliability. However, as soon as the environment becomes more complex — variations in shapes, lighting, textures, positioning — rule-based image processing systems reach their limits. Subtle defects, organic objects, or products with high variability cannot easily be described using fixed rules. When inspection tasks become too complex, deep learning provides a relevant alternative. Generally speaking, the more intuitive a task is for a human based on a simple image (without measurement tools), the more likely it is to be automated through AI.
AI and industrial vision: a data-driven approach
Deep learning uses an example-based rather than a rule-based approach. By leveraging neural networks to teach a computer what constitutes a good image based on a reference dataset, deep learning can analyse, locate, and classify objects, or even read printed markings. When unpredictability and natural variations are inherent to the process, deep learning technology comes into its own.
Artificial Intelligence techniques, particularly Deep Learning, are thus transforming the design and implementation of industrial vision systems. Unlike the symbolic approach, which requires manual definition of characteristics to monitor, AI-based systems learn to recognise patterns and make decisions from sets of annotated images.
In this supervised model, data becomes the central element. Annotated images — for instance, “OK part” vs. “defective part” — are used to train a neural network. The network automatically extracts discriminative features and learns to generalise this knowledge to new cases. This process drastically reduces the need for manual coding and enables domain experts (quality, production, healthcare, etc.) to directly contribute to application development by annotating data, without requiring programming skills.
The introduction of Edge Learning systems makes it possible to train and run models directly on embedded processing units within the cameras, eliminating the need for a PC or GPU.
Advantages of AI for industrial vision applications
• Flexibility and adaptability: unlike fixed rules, AI models can adapt to natural variations in production (appearance, position, lighting, etc.).
• Simplified development: no need to manually define detection criteria; the system learns by example.
• Ability to handle complex cases: AI is suitable for detecting aesthetic defects (scratches, dents, texture variations) that are difficult to model using rules.
• Reduced implementation time: models can be trained using only a few dozen or hundreds of examples.
• Robustness to drift: models can be updated to take into account natural changes in the production environment.
Key AI applications in industrial vision
• Aesthetic inspection: detection of scratches, dents, and irregularities on complex or reflective surfaces.
• Complex object recognition: identification of objects in unstructured or organic environments (e.g. agriculture, food industry, healthcare).
• Reading markings and characters: OCR on irregular or worn surfaces.
• Product classification: distinguishing between different variants or categories of a product, even with subtle differences.
• Element localisation: precise positioning despite variations in shape or background.
Written by Youssef BELGNAOUI, Editor-in-Chief of Automation France.
